Financial Services AI

Your traders are using ChatGPT. Your compliance team doesn’t know.

An analyst pastes a draft earnings release into ChatGPT. A relationship manager feeds a client’s portfolio to an AI. A research agent reaches into account systems over MCP. The regulated data leaves in the prompt — and comes back in the reply.

April 11, 20265 min read

It’s 6:47 AM. None of them think they’re doing anything wrong, and every one of them just moved confidential data to a service with no obligation to protect it. Financial services adopts technology faster than almost any industry — and AI adoption is outpacing the controls meant to govern it.

The problem isn’t a single careless prompt. It’s that regulated data now moves on two AI paths at once, and the tooling built for email and file shares reads neither.

Employees using AI

In the prompt.

Draft earnings, client portfolios, account numbers, proprietary models — pasted into ChatGPT, Claude, Gemini and Copilot as free-form text over ordinary HTTPS. Email surveillance and file DLP never see it.

Agents you run

In the reply.

A research or servicing agent calls an internal tool over MCP and an account record comes straight back into the model’s context — the sharper version of the same leak, on standing access.

BlueAspen is data security for AI: one policy engine and one detection stack across both surfaces. You define what counts as sensitive — account and card data, client PII, credentials, source for a proprietary model — and BlueAspen enforces it inline, at the moment of the call.

Detection with mathematical precision

In financial services, false positives aren’t a nuisance — they breed alert fatigue until real findings get waved through. So BlueAspen proves a match instead of guessing at it:

validated detection · the check runs, not a guess
4111 1111 1111 1111
card passes the Luhn checksum → PCI finding
PASS
4111 1111 1111 1112
checksum fails → no false finding
FAIL
Also: ABA checksums for routing numbers, mod-97 for IBANs, SWIFT country prefixes, and Shannon entropy plus JWT decoding for secrets — 60+ context-scored patterns, each finding carrying the rule that fired.

Enforce without stopping the desk

For people using AI, enforcement lives on the request leg. A prompt carrying an account number and portfolio detail can have the sensitive fields redacted in place — the account number becomes [REDACTED] while the analytical question still reaches the model. This surface fails open by design: a policy outage never blocks someone from working.

On the paths where an agent acts on data — agent to MCP tool, agent to agent, or an MCP server you expose — the posture flips to fail-closed. A call BlueAspen can’t judge never reaches the data, and a misconfigured gateway refuses to start. And on the result leg, the record a tool returns is screened and redacted in place before the agent or model sees it — the half most controls never look at.

The supervisory record you can hand an examiner

Every interaction carries full context — provider, caller, findings, decision, timestamp — with sensitive values redacted, so the evidence trail isn’t itself a liability. Regulators increasingly expect firms to supervise AI use with the same rigor as other electronic communications; BlueAspen gives you the record and the control to do it, and exports to the SIEM your firm already runs. It detects PCI, PII and secrets and complements your existing DLP, CASB and AI gateway rather than replacing them.

Catch regulated data before it reaches the model.

A 20-minute visibility assessment shows the AI traffic already leaving your firm, and the regulated data going with it.

Book an assessment →